Situation-aware User Interest Mining on Mobile Handheld Devices

We present our experience in creating a novel unsupervised clustering algorithm for situation-aware pattern extraction from usage logs. The algorithm automatically estimates nearoptimal number of clusters and cluster centroids. It models situation by taking advantage of sensors. 5-fold cross validations using real-world data show that the algorithm delivers higher accuracy than existing algorithms with much lower complexity. As a result it is the first clustering algorithm that can be practically deployed on mobile handheld devices in the real world. We also describe the research problems in situation-aware personalization that must be addressed before users can benefit from the learning algorithms and speculate on possible approaches to the solutions.